Bang Sunjoo, Son Sangjoon, Roh Hyunwoong, Lee Jihye, Bae Sungyun, Lee Kyungwon, Hong Changhyung, Shin Hyunjung
Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea.
Department of Psychiatry, Ajou University School of Medicine, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea.
BMC Med Inform Decis Mak. 2017 May 18;17(Suppl 1):60. doi: 10.1186/s12911-017-0451-3.
The number of people with dementia is increasing along with people's ageing trend worldwide. Therefore, there are various researches to improve a dementia diagnosis process in the field of computer-aided diagnosis (CAD) technology. The most significant issue is that the evaluation processes by physician which is based on medical information for patients and questionnaire from their guardians are time consuming, subjective and prone to error. This problem can be solved by an overall data mining modeling, which subsidizes an intuitive decision of clinicians.
Therefore, in this paper we propose a quad-phased data mining modeling consisting of 4 modules. In Proposer Module, significant diagnostic criteria are selected that are effective for diagnostics. Then in Predictor Module, a model is constructed to predict and diagnose dementia based on a machine learning algorism. To help clinical physicians understand results of the predictive model better, in Descriptor Module, we interpret causes of diagnostics by profiling patient groups. Lastly, in Visualization Module, we provide visualization to effectively explore characteristics of patient groups.
The proposed model is applied for CREDOS study which contains clinical data collected from 37 university-affiliated hospitals in republic of Korea from year 2005 to 2013.
This research is an intelligent system enabling intuitive collaboration between CAD system and physicians. And also, improved evaluation process is able to effectively reduce time and cost consuming for clinicians and patients.
随着全球人口老龄化趋势加剧，痴呆症患者数量不断增加。因此，在计算机辅助诊断（CAD）技术领域有各种旨在改进痴呆症诊断流程的研究。最突出的问题是，医生基于患者医疗信息和其监护人问卷进行的评估过程既耗时、主观又容易出错。这个问题可以通过全面的数据挖掘建模来解决，该建模为临床医生的直观决策提供支持。
因此，在本文中我们提出了一种由4个模块组成的四阶段数据挖掘建模方法。在提议者模块中，选择对诊断有效的重要诊断标准。然后在预测器模块中，基于机器学习算法构建一个用于预测和诊断痴呆症的模型。为了帮助临床医生更好地理解预测模型的结果，在描述符模块中，我们通过对患者群体进行剖析来解释诊断原因。最后，在可视化模块中，我们提供可视化以有效探索患者群体的特征。
所提出的模型应用于CREDOS研究，该研究包含了2005年至2013年从韩国37家大学附属医院收集的临床数据。
本研究是一个智能系统，能够实现CAD系统与医生之间的直观协作。此外，改进后的评估流程能够有效减少临床医生和患者的时间和成本消耗。
